Manufacturers in the automotive industry are striving continually to provide vehicles with enhanced design and performance features in order to meet with the growing demands of consumers. Furthermore, the sector must comply with increasingly strict government regulations which set out minimum requirements in terms of vehicle safety and fuel economy. Vehicle manufacturers must therefore produce new vehicle models which not only comply with fuel efficiency and safety standards, but also incorporate innovative technologies to attract consumers to their products. These factors all act to impact the time and cost required to develop new vehicles, for which manufacturers have strict targets to meet if they are to stay relevant in the competitive global automotive market.
It is therefore important for vehicle manufacturers to reduce the time and cost associated with the vehicle development cycle. A widely used method for achieving this is the use of vehicle simulation. Vehicle simulation is a valuable tool used during the test and development cycle which allows vehicle behaviour to be modelled so as to ensure that vehicle developments have the desired effect on performance. Vehicle simulation improves the efficiency of the development and testing process which in turn reduces time and expenditure. For this reason, vehicle simulation is used extensively within the automotive industry.
Computer-implemented or ‘virtual’ vehicle simulations are advantageous in a number of ways. In physical testing, the need for fully assembled test cars, drivers, venues and fuel leads to very high costs, acting as a barrier to automotive companies using these techniques. This gives virtual testing a major advantage over physical testing, allowing a large number of variables to be included and tests to be undertaken at significantly reduced expense. A further benefit of using virtual simulation is that it allows for improved repeatability when compared with physical testing, where it may be difficult to exactly reproduce a given test situation/environment.
In order for virtual vehicle simulation to be effective, high quality test data must be available for use in the vehicle models. To build a full vehicle model which accurately represents the performance of the vehicle, it is necessary to model the various components of the vehicle. One of the most challenging components to model is the tyre, which exhibits complex, non-linear behaviour.
There are a number of models which can be used to simulate tyre performance, one of which is known as the ‘Magic Formula’ (MF) model. This model was developed through collaboration between Volvo cars and Delft University of Technology and has become the benchmark for tyre modelling, utilised widely in the automotive industry, as well as across academia. The MF model is an empirical model which predicts tyre behaviour using data gathered from tyre testing. For this, the model provides a set of mathematical formulae from which the forces and moments acting from the road to the tyre can be simulated for both steady-state and transient tyre behaviour, for pure lateral cornering, braking and driving, in addition to combined handling conditions. Since the MF model was first developed, it has gone through various iterations where additional features have been added and improvements have been made, with the most up-to-date version being MF6.1.
To obtain tyre data for use in the MF model and others like it, specially designed test rigs are used which can test a range of tyre types including passenger car, light truck, SUV and motorsport tyres. These rigs typically consist of a pair of drums around which a steel belt is wrapped to provide the tyre testing area on which an appropriate test surface (such as sandpaper) is affixed. For testing, the tyre is rotatably mounted onto a control arm. In these set-ups, variables such as speed, load, inflation pressure and true tyre motion relative to the road surface can be controlled, allowing for a broad range of tyre behaviour to be measured. A major concern for the automotive industry in terms of tyre testing is the expense associated with renting one of these rigs for use. It is, of course, advantageous for tyre data to be collected efficiently in any case, but with the added consideration of expensive rig time, it becomes even more beneficial to obtain the most data in the shortest possible time, without compromising on quality. For this, an efficient and accurate test procedure is required.
There are various existing methods for testing tyres to gather data for use with, that is to say, to ‘parameterise’, the MF6.1 model. One such test procedure is the square matrix approach. The square matrix approach uses a set of constant loads, cambers and inflation pressures, and collects data across a range of slip angles and slip ratios. The chosen slip angle range is swept across at each of the chosen constant load cases under free rolling conditions (when there is no braking or acceleration). To obtain longitudinal data, sweeps are conducted across the chosen slip ratio range at each of the chosen constant load cases for a constant slip angle. Combined data is gathered by repeating the data sweeps conducted across the slip ratio range at typically three constant slip angles. A clear disadvantage of this method is the high number of sweeps that are required to collect the necessary data. For example, given four loads, three cambers, three inflation pressures, and combined testing at three different slip angles, the square matrix approach would require a total of 144 separate sweeps to be undertaken, which is time consuming and, hence, costly.
In general, there is a need to develop tyre testing methods and techniques which improve the efficiency of the data gathering exercise whilst maintaining and even improving the quality of data that is acquired.
It is against this background that the invention has been devised.